There Is a New Fad in Big Data Analysis: Python

The term ‘Big Data’ is used to refer to a large volume of data. There is much debate surrounding this term, so it is worth staying informed on the latest developments in Big Data for the upcoming year.

If the crystal ball is any indication, here are some exciting developments in Big Data to keep an eye on in the next year:

  • By integrating previously siloed datasets, big data has shifted to broad data.
  • Data competency is the result of both data synthesis and analysis.
  • Customer analytics as a service.
  • Assisting analytical systems in finding patterns in data will be algorithms.
  • There has been an enhancement to the voice recognition system, which will result in enhanced user engagement.
  • Intelligent metadata catalogs will be developed with the help of machine learning.
  • Researchers studying the climate will make extensive use of big data.
  • In the near future, businesses in certain fields will need the ability to analyze data in real time.

In the near future, there are set to be some substantial changes which could have a major impact on the way businesses operate. Additionally, the utilisation of Big Data is being made easier for large companies, with Python being an integral part of this process.

It is correct to say that Python, a language traditionally used for website and online application creation, is now the popular choice for Big Data. But what has caused this sudden trend? What makes Python so well suited to managing large amounts of data? Let us analyse this further.

Simplicity of usage

Python is widely recognised for its intuitive nature, making it an ideal language for those looking to make a start with Big Data. This means that, for your organisation, the learning curve for your development teams will be much less steep than with many other languages, saving time and resources.

As a programming language, what makes Python so accessible to learn? Its syntax, which utilises English and is straightforward to understand without a detailed knowledge of computer science or software engineering, is certainly advantageous. Additionally, the lack of a compulsory compiler makes Python even more appealing as it allows users to both create and execute code.

The widespread availability of Python implies that it can be used to create programs and scripts on almost any device.

Open-source

Python is free and accessible to all. Open source software is defined as software with freely available source code that can be seen, modified, and redistributed by anyone. The significance of big data is further highlighted by the fact that many businesses have adopted open source software to power their supply chains. The advantage of open source software is that it is much simpler to integrate with existing software and processes.

The interoperability of Big Data solutions such as NoSQL databases is of paramount importance. The use of an open-source language like Python facilitates this process and makes it both achievable and straightforward.

Enormous Collection Ideal for Big Data

The proliferation of Python modules tailored specifically to large data analysis is a major factor propelling the Python/Big Data movement.

The top Python libraries for Big Data include:

  • Panda is a powerful toolkit designed to facilitate data analysis. It provides easy access to common data structure operations for working with tabular data and time series.
  • Numpy is a Python library specially designed for scientific computing. It enables high-level mathematical operations such as linear algebra, random number generation, Fourier transformations, multi-dimensional arrays and matrices.
  • SciPy modules can be utilised for many scientific and engineering applications, such as optimisation, linear algebra, integration, interpolation, fast Fourier transform (FFT), signal and image processing, and ODE solvers.
  • Mlpy is a machine learning toolkit that builds on the functionality of NumPy and SciPy, providing a balanced approach to modularity, repeatability, maintainability, usability and efficiency.
  • Matplotlib enables the creation of 2D plots, charts, histograms, error charts, power spectra and scatter plots, which can be published in a range of print formats.
  • Theano is a numerical computing library that may be used to optimize and define functions, as well as evaluate mathematical expressions.
  • To investigate graphs, you may utilize the NetworkX package.
  • SymPy’s symbolic computation toolkit is an open-source library for parallel computing, which covers basic symbolic arithmetic, calculus, algebra, discrete mathematics and quantum physics.
  • Dmelt is used in the fields of large data statistics and computational mathematics.
  • Besides TensorFlow, another machine learning package called Scikit-learn offers features including regression and clustering techniques.

Assist in handling of visual and auditory information

In the future, Big Data will not be restricted to just numerical and textual data. As technology advances, Big Data will have to accommodate multimedia such as images and audio files. We can already see how popular virtual assistants like Google Now, Apple’s Siri, and Amazon’s Alexa are becoming. These requests are not stored on the servers, but must be responded to instantly.

Python provides an impressive solution to complex problems, largely thanks to its capacity to handle both images and data through a range of modules.

Able to Function in Hadoop

Python is widely used and is compatible with Hadoop, making it a useful tool. What does this mean? Hadoop is an open-source Java framework which enables clusters of computers to be used to solve problems which require the processing of large amounts of data, otherwise known as ‘Big Data’.

By utilising Hadoop, large businesses can realise significant cost savings by constructing large clusters on commodity hardware (as opposed to investing heavily in servers) to process huge data sets.

Python’s integration with Hadoop Streaming makes it easy to use any executable or script as the mapper and/or reducer in a Map/Reduce job. This is a hugely beneficial feature for any Big Data project.

Conclusion

It is not too late for your organization to utilize Big Data. It is recommended that you have a team of Python developers available to assist with this significant undertaking, as this will enable your company to make use of Big Data in innovative and beneficial ways.

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